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Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; EMNLP 2021 ; https://hal.archives-ouvertes.fr/hal-03480371 ; EMNLP 2021, Nov 2021, Punta Cana (online), Dominican Republic. pp.10438-10449 (2021)
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QuestEval: Summarization Asks for Fact-based Evaluation
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.sorbonne-universite.fr/hal-03541895 ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Nov 2021, Punta Cana (en ligne), Dominican Republic. pp.6594-6604, ⟨10.18653/v1/2021.emnlp-main.529⟩ ; https://2021.emnlp.org/ (2021)
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Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction ...
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Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction ...
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The Rare Word Issue in Natural Language Generation: A Character-Based Solution
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
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In: ECIR 2020 - 42nd European Conference on Information Retrieval ; https://hal.archives-ouvertes.fr/hal-02503463 ; ECIR 2020 - 42nd European Conference on Information Retrieval, Apr 2020, Lisbon, Portugal (2020)
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Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction!
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.archives-ouvertes.fr/hal-03480380 ; Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020, Punta Cana (Online), Dominican Republic. pp.3689-3701, ⟨10.18653/v1/2020.emnlp-main.301⟩ ; https://aclanthology.org/2020.emnlp-main.301 (2020)
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization ...
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
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Incorporating Visual Semantics into Sentence Representations within a Grounded Space
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In: 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing ; https://hal.sorbonne-universite.fr/hal-02351003 ; 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Nov 2019, Hong Kong, China. pp.696-707, ⟨10.18653/v1/D19-1064⟩ (2019)
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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.sorbonne-universite.fr/hal-01156600 ; BMC Bioinformatics, BioMed Central, 2015, 16 (1), pp.138. ⟨10.1186/s12859-015-0564-6⟩ (2015)
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Coping with the Document Frequency Bias in Sentiment Classification
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 6 No. 1 (2012): Sixth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2012)
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Multiview Clustering of Multilingual Documents
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In: Proceedings of the 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; https://hal.archives-ouvertes.fr/hal-01292100 ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010), Jul 2010, Geneva, Switzerland. pp.812-822, ⟨10.1145/1835449.1835633⟩ (2010)
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Improving Image Annotation in Imbalanced Classification Problems with Ranking SVM
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In: Multilingual Information Access Evaluation II. Multimedia Experiments ; CLEF 2009 - 10th Workshop of the Cross-Language Evaluation Forum ; https://hal.archives-ouvertes.fr/hal-00581661 ; CLEF 2009 - 10th Workshop of the Cross-Language Evaluation Forum, Sep 2009, Corfu, Greece. pp.291-294, ⟨10.1007/978-3-642-15751-6_37⟩ (2009)
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Abstract:
International audience ; We try to overcome the imbalanced data set problem in image annotation by choosing a convenient loss function for learning the classifier. Instead of training a standard SVM, we use a Ranking SVM in which the chosen loss function is helpful in the case of imbalanced data. We compare the Ranking SVM to a classical SVM with different visual features. We observe that Ranking SVM always improves the prediction quality, and can perform up to 23% better than the classical SVM.
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Keyword:
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
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URL: https://hal.archives-ouvertes.fr/hal-00581661 https://doi.org/10.1007/978-3-642-15751-6_37
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A Comparative Study of Diversity Methods for Hybrid Text and Image Retrieval Approaches
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In: Evaluating Systems for Multilingual and Multimodal Information Access ; 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008 ; https://hal.archives-ouvertes.fr/hal-00581664 ; 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Sep 2008, Aarhus, Denmark. pp.585-592, ⟨10.1007/978-3-642-04447-2_72⟩ (2008)
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The XML Wikipedia Corpus
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In: ISSN: 0163-5840 ; Sigir Forum ; https://hal.archives-ouvertes.fr/hal-01172244 ; Sigir Forum, Association for Computing Machinery (ACM), 2006, 40 (1), pp.64-69. ⟨10.1145/1147197.1147210⟩ (2006)
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A Machine Learning based Approach to Evaluating Retrieval Systems
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In: Proc. Human Language Technology Conference - HLT-NAACL'06 ; https://hal.archives-ouvertes.fr/hal-01352077 ; Proc. Human Language Technology Conference - HLT-NAACL'06, Jun 2006, New-York, United States. pp.399-406, ⟨10.3115/1220835.1220886⟩ (2006)
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Structured Multimedia Document Classification
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In: ACM Document Engeneering ; https://hal.archives-ouvertes.fr/hal-01357593 ; ACM Document Engeneering, Nov 2003, Grenoble, France. pp.153-160, ⟨10.1145/958220.958249⟩ (2003)
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